Luis Magdalena

Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases

Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.

Ten years of genetic fuzzy systems: current framework and new trends

Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems. After a brief introduction to models and applications of genetic fuzzy systems, the field is overviewed, new trends are identified, a critical evaluation of genetic fuzzy systems for fuzzy knowledge extraction is elaborated, and open questions that remain to be addressed in the future are raised. The paper also includes some of the key references required to quickly access implementation details of genetic fuzzy systems.

Accuracy Improvements in Linguistic Fuzzy Modeling

Overview.- Accuracy Improvements to Find the Balance Interpretability-Accuracy in Linguistic Fuzzy Modeling: An Overview.- Accuracy Improvements Constrained by Interpretability Criteria.- COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy.- Constrained optimization of genetic fuzzy systems.- Trade-off between the Number of Fuzzy Rules and Their Classification Performance.- Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms.- Fuzzy CoCo: Balancing Accuracy and Interpretability of Fuzzy Models by Means of Coevolution.- On the Achievement of Both Accurate and Interpretable Fuzzy Systems Using Data-Driven Design Processes.- Extending the Modeling Process to Improve the Accuracy.- Linguistic Hedges and Fuzzy Rule Based Systems.- Automatic Construction of Fuzzy Rule-Based Systems: A trade-off between complexity and accuracy maintaining interpretability.- Using Individually Tested Rules for the Data-based Generation of Interpretable Rule Bases with High Accuracy.- Extending the Model Structure to Improve the Accuracy.- A description of several characteristics for improving the accuracy and interpretability of inductive linguistic rule learning algorithms.- An Iterative Learning Methodology to Design Hierarchical Systems of Linguistic Rules for Linguistic Modeling.- Learning Default Fuzzy Rules with General and Punctual Exceptions.- Integration of Fuzzy Knowledge.- Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?.

Looking for a good fuzzy system interpretability index: An experimental approach

Interpretability is acknowledged as the main advantage of fuzzy systems and it should be given a main role in fuzzy modeling. Classical systems are viewed as black boxes because mathematical formulas set the mapping between inputs and outputs. On the contrary, fuzzy systems (if they are built regarding some constraints) can be seen as gray boxes in the sense that every element of the whole system can be checked and understood by a human being. Interpretability is essential for those applications with high human interaction, for instance decision support systems in fields like medicine, economics, etc. Since interpretability is not guaranteed by definition, a huge effort has been done to find out the basic constraints to be superimposed during the fuzzy modeling process. People talk a lot about interpretability but the real meaning is not clear. Understanding of fuzzy systems is a subjective task which strongly depends on the background (experience, preferences, and knowledge) of the person who makes the assessment. As a consequence, although there have been a few attempts to define interpretability indices, there is still not a universal index widely accepted. As part of this work, with the aim of evaluating the most used indices, an experimental analysis (in the form of a web poll) was carried out yielding some useful clues to keep in mind regarding interpretability assessment. Results extracted from the poll show the inherent subjectivity of the measure because we collected a huge diversity of answers completely different at first glance. However, it was possible to find out some interesting user profiles after comparing carefully all the answers. It can be concluded that defining a numerical index is not enough to get a widely accepted index. Moreover, it is necessary to define a fuzzy index easily adaptable to the context of each problem as well as to the user quality criteria.

VIRTUOUS: vision-based road transportation for unmanned operation on urban-like scenarios

This work presents an intelligent transportation system (ITS) that was implemented on an autonomous vehicle designed to perform global navigation missions on a network of unmarked roads. This is the first step toward the complete implementation of ITS in urban environments, which is the long-term goal of this work. Using a global positioning system, global navigation is achieved by means of a global planner and a task manager that recurrently coordinate the execution of vision-based perception tasks for the road tracking of nonstructured roads and the navigation of intersections. In addition, a vision-based vehicle-detection task has been developed, which endows the global navigation system with a reactive capacity. The complete system has been tested on the BABIECA prototype vehicle, which was autonomously driven for hundreds of kilometers around a private circuit, designed to emulate an urban quarter, at speeds of up to 50 km/h, successfully carrying out different navigation missions. During the tests, the vehicle drove itself across crossroads and performed the appropriate turning maneuvers at intersections. It also demonstrated its robustness with regard to shadows, road texture, weather conditions, and changing illumination.

Editorial: Special issue on interpretable fuzzy systems

Interpretability is acknowledged as one of the most appreciated advantages of fuzzy systems in many applications, especially in those with high human interaction where it actually becomes a strong requirement. However, it is important to remark that there is a somehow misleading but widely extended belief, even in part of the fuzzy community, regarding fuzzy systems as interpretable no matter how they were designed. Of course, we are aware the use of fuzzy logic favors the interpretability of designed models. Thanks to their semantic expressivity, close to natural language, fuzzy variables and rules can be used to formalize linguistic propositions which are likely to be easily understandood by human beings. Obviously, this fact makes easier the knowledge extraction and representation tasks carried out when modeling real-world complex systems. Notwithstanding, fuzzy logic is not enough by itself to guarantee the interpretability of the final model. As it is thoroughly illustrated in this special issue, achieving interpretable fuzzy systems is a matter of careful design because fuzzy systems cannot be deemed as interpretable per se. Thus, several constraints have to be imposed along the whole design process with the aim of producing really interpretable fuzzy systems, in the sense that every element of the whole system may be checked and understood by a human being. Otherwise, fuzzy systems may even become black-boxes.

HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers

This work presents a methodology for building interpretable fuzzy systems for classification problems. We consider interpretability from two points of view: (1) readability of the system description and (2) comprehensibility of the system behavior explanations. The fuzzy modeling methodology named as Highly Interpretable Linguistic Knowledge (HILK) is upgraded. Firstly, a feature selection procedure based on crisp decision trees is carried out. Secondly, several strong fuzzy partitions are automatically generated from experimental data for all the selected inputs. For each input, all partitions are compared and the best one according to data distribution is selected. Thirdly, a set of linguistic rules are defined combining the previously generated linguistic variables. Then, a linguistic simplification procedure guided by a novel interpretability index is applied to get a more compact and general set of rules with a minimum loss of accuracy. Finally, partition tuning based on two efficient search strategies increases the system accuracy while preserving the high interpretability. Results obtained in several benchmark classification problems are encouraging because they show the ability of the new methodology for generating highly interpretable fuzzy rule-based classifiers while yielding accuracy comparable to that achieved by other methods like neural networks and C4.5. The best configuration of HILK will depend on each specific problem under consideration but it is important to remark that HILK is flexible enough (thanks to the combination of several algorithms in each modeling stage) to be easily adaptable to a wide range of problems.

What is Soft Computing? Revisiting Possible Answers

The term Soft Computing was coined by L.A. Zadeh in the early 90s. Since that time many researchers have tried to define it considering different approaches: main constituents, properties, abilities, etc. In addition, the term Computational Intelligence has also gained popularity having a somehow quite close meaning to that of Soft Computing. The central idea of this paper is to present, analyze, compare and discuss a few of the definitions that can be found on literature; not trying to find the best but to offer the reader arguments to make his/her own decision.

Generating understandable and accurate fuzzy rule-based systems in a java environment

Looking for a good interpretability-accuracy trade-off is one of the most challenging tasks on fuzzy modelling. Indeed, interpretability is acknowledged as a distinguishing capability of linguistic fuzzy systems since the proposal of Zadeh and Mamdanis seminal ideas. Anyway, obtaining interpretable fuzzy systems is not straightforward. It becomes a matter of careful design which must cover several abstraction levels. Namely, from the design of each individual linguistic term (and its related fuzzy set) to the analysis of the cooperation among several rules, what depends on the fuzzy inference mechanism. This work gives an overview on existing tools for fuzzy system modelling. Moreover, it introduces GUAJE which is an open-source free-software java environment for building understandable and accurate fuzzy rule-based systems by means of combining several pre-existing tools.

Genetic fuzzy systems. New developments

While preparing our participation in the Joint IFSA-NAFIPS 2001 International Conference, working in the organization of a mini-track on genetic fuzzy systems, we realized that 2001 was the tenth anniversary of the -rst publications on genetic fuzzy systems. The papers by Karr [3], Pham and Karaboga [4], Thrift [5], and Valenzuela-Rend6 on [6], were all published in 1991. For that reason, the mini-track included the paper “Ten years of genetic fuzzy systems: Current framework and new trends”, as an attempt to summarize previous research and to open a view to the future of the -eld. In addition, 2001 was also the date when the -rst authored book on the state of the art of genetic fuzzy systems was published [1], 1 almost simultaneously with the celebration of the Joint IFSA-NAFIPS Conference (Vancouver). Considering that date as a sort of milestone, we decided to compile in a special issue (this one) some papers describing the most recent and innovative works in the -eld, as well as to advert attention to open questions and to identify future trends in genetic fuzzy systems. The result was a call for papers for a special issue on “Genetic Fuzzy Systems: New Developments”, receiving 10 submissions, some of them resulting from extended versions of papers presented at the previously mentioned mini-track, while others directly submitted to the special issue. One of the guest editors, plus two additional reviewers (whose work and cooperation we want to acknowledge and thank) carefully reviewed each of the ten manuscripts. The Co-Editors-inChief of Fuzzy Sets and Systems conducted reviews of papers authored or co-authored by any of the guest editors. At the end, only six of the ten submitted contributions were accepted for publication. The six papers on this special issue address three distinct subjects. An introductory paper provides an overview of the current state-of-the art of the -eld and a perspective on its future. The next three address learning of fuzzy classi-cation systems, two of them introducing boosting techniques in the realm of genetic fuzzy systems, and one based on multi-objective genetic selection. The last two papers deal with a hierarchical co-evolutionary structure to evolve fuzzy models, and an application concerning autonomous navigation of mobile robots, respectively. The papers contents are summarized next.